TY - JOUR
T1 - onlineforecast: An R package for adaptive and recursive forecasting
AU - Bacher, Peder
AU - Bergsteinsson, Hjörleifur G.
AU - Frölke, Linde
AU - Sørensen, Mikkel L.
AU - Lemos-Vinasco, Julian
AU - Liisberg, Jon
AU - Møller, Jan Kloppenborg
AU - Nielsen, Henrik Aalborg
AU - Madsen, Henrik
PY - 2023
Y1 - 2023
N2 - Systems that rely on forecasts to make decisions, e.g. control or energy
trading systems, require frequent updates of the forecasts. Usually, the
forecasts are updated whenever new observations become available, hence in an
online setting. We present the R package onlineforecast that provides a
generalized setup of data and models for online forecasting. It has
functionality for time-adaptive fitting of linear regression-based models.
Furthermore, dynamical and non-linear effects can be easily included in the
models. The setup is tailored to enable effective use of forecasts as model
inputs, e.g. numerical weather forecast. Users can create new models for their
particular system applications and run models in an operational online setting.
The package also allows users to easily replace parts of the setup, e.g. use
kernel or neural network methods for estimation. The package comes with
comprehensive vignettes and examples of online forecasting applications in
energy systems, but can easily be applied in all fields where online
forecasting is used.
AB - Systems that rely on forecasts to make decisions, e.g. control or energy
trading systems, require frequent updates of the forecasts. Usually, the
forecasts are updated whenever new observations become available, hence in an
online setting. We present the R package onlineforecast that provides a
generalized setup of data and models for online forecasting. It has
functionality for time-adaptive fitting of linear regression-based models.
Furthermore, dynamical and non-linear effects can be easily included in the
models. The setup is tailored to enable effective use of forecasts as model
inputs, e.g. numerical weather forecast. Users can create new models for their
particular system applications and run models in an operational online setting.
The package also allows users to easily replace parts of the setup, e.g. use
kernel or neural network methods for estimation. The package comes with
comprehensive vignettes and examples of online forecasting applications in
energy systems, but can easily be applied in all fields where online
forecasting is used.
KW - Recursive estimation
KW - Adaptive
KW - Non-linear transformation
KW - Time series
KW - Energy
KW - Online Forecasting
KW - Prediction
KW - R
M3 - Journal article
SN - 1548-7660
JO - Journal of Statistical Software
JF - Journal of Statistical Software
ER -